Flexible Energy-Efficient Implementation of Adaptive Spiking Encoder for Neuromorphic Processors

Neuromorphic computing could pave the way to a new generation of smart sensors that can process signals locally through Spiking Neural Networks (SNNs). For this paradigm to take hold, it is necessary to have an analog-to-spike encoder adaptable to a wide range of applications. The encoding system should offer the possibility to try different encoding algorithms, giving freedom to the designers to select the most appropriate approach for the target task. At the same time, it should feature a tunable parameter to modulate the spike density, in the pursuit of a compromise between accuracy and power consumption. Therefore, the goal of this work is to provide a platform enabling the conversion of analog signals to a sequence of spikes, characterized by flexibility, high energy efficiency, and small area. We introduce an encoder designed and simulated in a standard 0.18 μ-m CMOS process which benefits from a switch- capacitor and a dynamic comparator to achieve very high energy efficiency. The controller unit can switch between Slope-based or Step-Forward Encoding algorithms. The encoder consumes 30 fJ/spike at 1.5 V supply voltage and occupies an area of 0.00325 mm
²
.